Visualization Lab 5

Assignment 1

1.1

Word cloud for OneTwo.txt

# word cloud for OneTwo.txt
df1$doc_id=1:nrow(df1)
colnames(df1)[1]<-"text"
#Here we interpret each line in the document as separate document
mycorpus <- Corpus(DataframeSource(df1)) #Creating corpus (collection of text data)
mycorpus <- tm_map(mycorpus, removePunctuation)
mycorpus <- tm_map(mycorpus, function(x) removeWords(x, stopwords("english")))
tdm <- TermDocumentMatrix(mycorpus) #Creating term-document matrix
m <- as.matrix(tdm)
#here we merge all rows
v <- sort(rowSums(m),decreasing=TRUE) #Sum up the frequencies of each word
d <- data.frame(word = names(v),freq=v) #Create one column=names, second=frequences
pal <- brewer.pal(8,"Dark2")
pal <- pal[-(1:2)] #Create palette of colors
wordcloud(d$word,d$freq, scale=c(8,.3),min.freq=2,max.words=100, random.order=F, rot.per=.15, colors=pal, vfont=c("sans serif","plain"))

Word cloud for Five.txt

# wordcloud for Five.txt
df2$doc_id=1:nrow(df2)
colnames(df2)[1]<-"text"
#Here we interpret each line in the document as separate document
mycorpus <- Corpus(DataframeSource(df2)) #Creating corpus (collection of text data)
mycorpus <- tm_map(mycorpus, removePunctuation)
mycorpus <- tm_map(mycorpus, function(x) removeWords(x, stopwords("english")))
tdm <- TermDocumentMatrix(mycorpus) #Creating term-document matrix
m <- as.matrix(tdm)
#here we merge all rows
v <- sort(rowSums(m),decreasing=TRUE) #Sum up the frequencies of each word
d <- data.frame(word = names(v),freq=v) #Create one column=names, second=frequences
pal <- brewer.pal(5,"Dark2")
pal <- pal[-(1:2)] #Create palette of colors
wordcloud(d$word,d$freq, scale=c(8,.3),min.freq=2,max.words=100, random.order=F, rot.per=.15, colors=pal, vfont=c("sans serif","plain"))

1.2

Phrase nets for Five.Txt and OneTwo.Txt with connector words

Phrase net for Five.txt

Phrase net for OneTwo.txt

1.3

Word Trees

Assignment 2

# Olive data
olive <- read.csv("olive.csv", sep = ",", header = TRUE)
# convert region to factor
olive$Region <-as.factor(olive$Region)

olive_shared <- SharedData$new(olive)

2.1

eic_lin_scatt <- olive_shared %>% 
  plot_ly(x = ~linoleic, y =~ eicosenoic) #%>%
  #add_markers(list(size = 6, color = "orange"))

We found, for unusually low observations of Eicosenoic it takes the values of 1,2,and 3.

2.2

bar_chart <- olive_shared %>%
  plot_ly( x =~ Region) %>%
  add_histogram() %>%
  layout(barmode = "overlay")
bar_chart

# linking the scatterplot and bar chart
subplot(eic_lin_scatt, bar_chart) %>%
  highlight(on = "plotly_select", dynamic = T, persistent = T, opacityDim = I(1)) %>%
  hide_legend()

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
Adding more colors to the selection color palette.
We recommend setting `persistent` to `FALSE` (the default) because persistent selection mode can now be used by holding the shift key (while triggering the `on` event).
Setting the `off` event (i.e., 'plotly_relayout') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.
Setting the `off` event (i.e., 'plotly_relayout') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

bscols(widths=c(2, NA),filter_slider("S", "Stearic", olive_shared, ~stearic)
        ,subplot(eic_lin_scatt, bar_chart)%>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%hide_legend())

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
Adding more colors to the selection color palette.
We recommend setting `persistent` to `FALSE` (the default) because persistent selection mode can now be used by holding the shift key (while triggering the `on` event).
Setting the `off` event (i.e., 'plotly_relayout') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.
Setting the `off` event (i.e., 'plotly_relayout') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Stearic

2.3

ara_lin_scatt <- olive_shared %>% 
  plot_ly(x = ~linolenic, y =~ arachidic)
subplot(eic_lin_scatt, ara_lin_scatt) %>%
  highlight(on = "plotly_select", dynamic = T, persistent = T, opacityDim = I(1)) %>%
  hide_legend()

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
Adding more colors to the selection color palette.
We recommend setting `persistent` to `FALSE` (the default) because persistent selection mode can now be used by holding the shift key (while triggering the `on` event).
Setting the `off` event (i.e., 'plotly_relayout') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.
Setting the `off` event (i.e., 'plotly_relayout') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

2.4

# the eight acids (column 4:11)
parcord <- ggparcoord(olive, columns = c(3:10))

# plotly_data returns data associated with a plotly visualization.
p_data <- plotly_data(ggplotly(parcord)) %>% group_by(.ID)

par_plot <- plot_ly(shared1, x = ~variable, y =~value) %>%
  add_lines(line = list(width = 0.3)) %>%
  add_markers(marker = list(size = 0.3),
              text = ~.ID, hoverinfo = "text") 

bscols(par_plot%>%highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
         hide_legend(),
       region_bar%>%highlight(on="plotly_click", dynamic=T, persistent = T)%>%hide_legend())

ps<-htmltools::tagList(par_plot%>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
  hide_legend(),
  region_bar%>%
    highlight(on="plotly_click", dynamic=T, persistent = T, opacityDim = I(1))%>%
    hide_legend()
  )
htmltools::browsable(ps)

# ignore this
subplot(par_plot, region_bar, nrows =2) %>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
  hide_legend()

Appendix

library(ggplot2)
library(plotly)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(crosstalk)
library(GGally)
library(htmltools)
df1<-read.table("OneTwo.txt",header=F, sep='\n') #Read file
df2<-read.table("Five.txt",header=F, sep='\n')
# word cloud for OneTwo.txt
df1$doc_id=1:nrow(df1)
colnames(df1)[1]<-"text"

#Here we interpret each line in the document as separate document
mycorpus <- Corpus(DataframeSource(df1)) #Creating corpus (collection of text data)
mycorpus <- tm_map(mycorpus, removePunctuation)
mycorpus <- tm_map(mycorpus, function(x) removeWords(x, stopwords("english")))
tdm <- TermDocumentMatrix(mycorpus) #Creating term-document matrix
m <- as.matrix(tdm)

#here we merge all rows
v <- sort(rowSums(m),decreasing=TRUE) #Sum up the frequencies of each word
d <- data.frame(word = names(v),freq=v) #Create one column=names, second=frequences
pal <- brewer.pal(8,"Dark2")
pal <- pal[-(1:2)] #Create palette of colors
wordcloud(d$word,d$freq, scale=c(8,.3),min.freq=2,max.words=100, random.order=F, rot.per=.15, colors=pal, vfont=c("sans serif","plain"))
# wordcloud for Five.txt

df2$doc_id=1:nrow(df2)
colnames(df2)[1]<-"text"

#Here we interpret each line in the document as separate document
mycorpus <- Corpus(DataframeSource(df2)) #Creating corpus (collection of text data)
mycorpus <- tm_map(mycorpus, removePunctuation)
mycorpus <- tm_map(mycorpus, function(x) removeWords(x, stopwords("english")))
tdm <- TermDocumentMatrix(mycorpus) #Creating term-document matrix
m <- as.matrix(tdm)

#here we merge all rows
v <- sort(rowSums(m),decreasing=TRUE) #Sum up the frequencies of each word
d <- data.frame(word = names(v),freq=v) #Create one column=names, second=frequences
pal <- brewer.pal(5,"Dark2")
pal <- pal[-(1:2)] #Create palette of colors
wordcloud(d$word,d$freq, scale=c(8,.3),min.freq=2,max.words=100, random.order=F, rot.per=.15, colors=pal, vfont=c("sans serif","plain"))
# Olive data
olive <- read.csv("olive.csv", sep = ",", header = TRUE)

# convert region to factor
olive$Region <-as.factor(olive$Region)
olive_shared <- SharedData$new(olive)
eic_lin_scatt <- olive_shared %>% 
  plot_ly(x = ~linoleic, y =~ eicosenoic) #%>%
  #add_markers(list(size = 6, color = "orange"))
bar_chart <- olive_shared %>%
  plot_ly( x =~ Region) %>%
  add_histogram() %>%
  layout(barmode = "overlay")

bar_chart
# linking the scatterplot and bar chart
subplot(eic_lin_scatt, bar_chart) %>%
  highlight(on = "plotly_select", dynamic = T, persistent = T, opacityDim = I(1)) %>%
  hide_legend()

bscols(widths=c(2, NA),filter_slider("S", "Stearic", olive_shared, ~stearic)
        ,subplot(eic_lin_scatt, bar_chart)%>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%hide_legend())
ara_lin_scatt <- olive_shared %>% 
  plot_ly(x = ~linolenic, y =~ arachidic)

subplot(eic_lin_scatt, ara_lin_scatt) %>%
  highlight(on = "plotly_select", dynamic = T, persistent = T, opacityDim = I(1)) %>%
  hide_legend()
# the eight acids (column 4:11)
parcord <- ggparcoord(olive, columns = c(3:10))
# plotly_data returns data associated with a plotly visualization.
p_data <- plotly_data(ggplotly(parcord)) %>% group_by(.ID)
# data for crosstalk
shared1<-SharedData$new(p_data, ~.ID, group = "Olive")
par_plot <- plot_ly(shared1, x = ~variable, y =~value) %>%
  add_lines(line = list(width = 0.3)) %>%
  add_markers(marker = list(size = 0.3),
              text = ~.ID, hoverinfo = "text") 

# subset
olive2 <- olive

# create an id
olive2$.ID <- 1:nrow(olive)

# shared data 2
shared2 <- SharedData$new(olive2, ~.ID, group = "olive")
# bar graph of region
region_bar <- shared2 %>%
  plot_ly( x =~ Region) %>%
  add_histogram() %>%
  layout(barmode = "overlay")

bscols(par_plot%>%highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
         hide_legend(),
       region_bar%>%highlight(on="plotly_click", dynamic=T, persistent = T)%>%hide_legend())
ps<-htmltools::tagList(par_plot%>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
  hide_legend(),
  region_bar%>%
    highlight(on="plotly_click", dynamic=T, persistent = T, opacityDim = I(1))%>%
    hide_legend()
  )
htmltools::browsable(ps)
# ignore this
subplot(par_plot, region_bar, nrows =2) %>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
  hide_legend()
# variable selection
ButtonsX=list()
for (i in 4:8){
  ButtonsX[[i-3]]= list(method = "restyle",
                        args = list( "x", list(olive2[[i]])),
                        label = colnames(olive2)[i])
}

ButtonsY=list()
for (i in 4:8){
  ButtonsY[[i-3]]= list(method = "restyle",
                        args = list( "y", list(olive2[[i]])),
                        label = colnames(olive2)[i])
}

ButtonsZ=list()
for (i in 4:8){
  ButtonsZ[[i-3]]= list(method = "restyle",
                        args = list( "z", list(olive2[[i]])),
                        label = colnames(olive2)[i])
}

plot3d <- plot_ly(shared2,x=~oleic, y=~linolenic, z=~palmitic)%>%
  add_markers() %>%
  layout(xaxis=list(title=""), yaxis=list(title="", zaxis =list(titles="")),
    title = "3d Scatter plot",
    updatemenus = list(
      list(x=0.07, y=0.6, buttons = ButtonsX, showactive = TRUE, method = "update" ),
      list(x =0.07, y=0.7, buttons = ButtonsY, showactive = TRUE, method = "update" ),
      list(x =0.07, y= .8, buttons = ButtonsZ, showactive = TRUE, method = "update" )
    ),
    annotations = list(
      list(text = "X", x= 0, y = 0.6, showarrow = FALSE),
      list(text = "Y", x = 0, y = 0.7, showarrow = FALSE),
      list(text = "Z", x = 0, y = .8, showarrow = FALSE)
    )
  
  )

plot3d

References